Detecting directed motion and confinement in single-particle trajectories using hidden variables Article Swipe
François Simon
,
Guillaume Ramadier
,
Inès Fonquernie
,
Janka Zsok
,
Sergiy Patskovsky
,
Michel Meunier
,
Caroline Boudoux
,
Elisa Dultz
,
Lucien E. Weiss
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.04.18.589798
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.04.18.589798
Single-particle tracking is a powerful tool for understanding protein dynamics and characterizing microenvironments. As the motion of unconstrained nanoscale particles is governed by Brownian diffusion, deviations from this behavior are biophysically insightful. However, the stochastic nature of particle movement and the presence of localization error pose a challenge for the robust classification of non-Brownian motion. Here, we present aTrack , a versatile tool for classifying track behaviors and extracting key parameters for particles undergoing Brownian, confined, or directed motion. Our tool quickly and accurately estimates motion parameters from individual tracks. Further, our tool can analyze populations of tracks and determine the most likely number of motion states. We show the working range of our approach on simulated tracks and demonstrate its application for characterizing particle motion in cells and for biosensing applications. aTrack is implemented as a stand-alone software, making it simple to analyze track data.
Related Topics
Concepts
Brownian motion
Tracking (education)
Particle (ecology)
Motion (physics)
Computer science
Range (aeronautics)
Statistical physics
Trajectory
Diffusion
Match moving
Artificial intelligence
Biological system
Physics
Algorithm
Computer vision
Mathematics
Engineering
Statistics
Geology
Pedagogy
Thermodynamics
Oceanography
Psychology
Biology
Aerospace engineering
Astronomy
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2024.04.18.589798
- https://www.biorxiv.org/content/biorxiv/early/2024/04/21/2024.04.18.589798.full.pdf
- OA Status
- green
- References
- 65
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4395006822
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4395006822Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1101/2024.04.18.589798Digital Object Identifier
- Title
-
Detecting directed motion and confinement in single-particle trajectories using hidden variablesWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-04-21Full publication date if available
- Authors
-
François Simon, Guillaume Ramadier, Inès Fonquernie, Janka Zsok, Sergiy Patskovsky, Michel Meunier, Caroline Boudoux, Elisa Dultz, Lucien E. WeissList of authors in order
- Landing page
-
https://doi.org/10.1101/2024.04.18.589798Publisher landing page
- PDF URL
-
https://www.biorxiv.org/content/biorxiv/early/2024/04/21/2024.04.18.589798.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.biorxiv.org/content/biorxiv/early/2024/04/21/2024.04.18.589798.full.pdfDirect OA link when available
- Concepts
-
Brownian motion, Tracking (education), Particle (ecology), Motion (physics), Computer science, Range (aeronautics), Statistical physics, Trajectory, Diffusion, Match moving, Artificial intelligence, Biological system, Physics, Algorithm, Computer vision, Mathematics, Engineering, Statistics, Geology, Pedagogy, Thermodynamics, Oceanography, Psychology, Biology, Aerospace engineering, AstronomyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
65Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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